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Learning under Distributed Weak Supervision
The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research
A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network
Deep learning models have had a great success in disease classifications
using large data pools of skin cancer images or lung X-rays. However, data
scarcity has been the roadblock of applying deep learning models directly on
prostate multiparametric MRI (mpMRI). Although model interpretation has been
heavily studied for natural images for the past few years, there has been a
lack of interpretation of deep learning models trained on medical images. This
work designs a customized workflow for the small and imbalanced data set of
prostate mpMRI where features were extracted from a deep learning model and
then analyzed by a traditional machine learning classifier. In addition, this
work contributes to revealing how deep learning models interpret mpMRI for
prostate cancer patients stratification
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